Technological indicators of market shift

TECHNOLOGICAL

FORECASTING

AND SOCIAL CHANGE 21,77-83

(1982)

Technological Indicators of Market Shift* JOSEPH

P. MARTIN0

ABSTRACT The past few years have seen many cases of displacement of domestic products by similar imported products, or by domestic products performing the same function but using a different technology. It would be desirable to forecast these technologically based market shifts before they have adverse effects on the industry whose products are being displaced. This paper describes research that shows that indicators of technological change (patents, papers, R&D expenditures) can provide from one to three years advanced warning of a market shift.

Introduction The goal of this research was to test several indicators of technology to determine whether they could serve as leading indicators of market shift. The concept was that if a market shift (e.g., an increase in imports) is caused by improved foreign technology, some prior indication of that technology might be used to give advanced warning of the market shift. We therefore examined a total of 11 instances of market shift. For each, we attempted to identify the specific nature of the technological change responsible for the market shift. We then examined indices of technological change to see whether they provided any advanced warning. The 11 cases we examined resulted from screening over 30 candidate cases. Most of these were rejected because of problems of obtaining the necessary data. The 11 cases we were able to examine provided us with a good estimate of the potential and the limitations of this approach to predicting market shifts. Background The idea of predicting technological change to be able to avoid adverse effects on industry dates back at least to 1964 (Ref. 1). However, the problems of measuring technological change are still severe. Work in technological forecasting has provided means for measuring and forecasting the technical parameters of individual machines. However, aggregated measures of the overall level of some technology are not available despite considerable effort. Instead of measures of technology, many researchers have worked with “indicaters . ’ ’ These are quantities that have a logical relationship with aggregate level of technology but are only an indirect measure of that level. Three of the most popular indicators are patents, technical papers, and R&D expenditures. Many prior researchers have shown these indicators to be related to technological change.

* This research was supported by the National Science Foundation are reported in Reference 3. JOSEPH P. MARTIN0 @J.

P. Martino,

1982

under Grant PRA78-203

13. Full results

is with the Research Institute, University of Dayton, Dayton, Ohio 45469.

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Some prior research has also shown a relationship between increases in foreign-held U.S. patents and subsequent increases in imports of the patented products. However, this prior research used highly aggregated measures of imports. It was only suggestive, since the import categories studied included a broad range of items, some of which might have been totally unrelated to the patents. Nevertheless, this prior research was encouraging. It led us to believe that by using a more refined measure of imports, and by testing a variety of indicators of technological change, we would be able to predict market shifts. Research Program We decided to examine ten specific cases of past market shift to determine whether indicators of technological change would have provided warning of those shifts. We wanted cases that met three criteria: 1. technological change had demonstrably occurred; 2. one or more of our indicators reflected that change; 3. a definite market shift did occur. We decided to focus on discrete commodities rather than entire industries. That is, we selected commodities we could define narrowly, and about which we could readily gather quantitative data. These became the “cases” to be studied. The majority of our cases involved increases in imports. However, we did include two cases of domestic market shift. The number of cases to be studied, ten, was based on the resources available. In all studies of this type, technology is treated as a “residual.” That is, all other possible causes of the observed changes are examined, and technology is invoked as an explanation only when these other causes are shown to be inadequate to explain all the changes that actually took place. In particular, market factors must first be eliminated before any remaining changes can be attributed to technology. For our import cases, we developed a model based on economic theory to explain changes in level of imports. Changes in imports beyond those explainable by economic theory could then be attributed to technological change. Our import model stated that the level of imports of a specific commodity from a specific country should be a function of the ratio of the price of the commodity from that country to the prices from all other countries, the ratio of the prices of the commodity from that country to the domestic U.S. price, and the demand for the commodity, as measured by some spending or income variable for a group of commodities that included the import item. From economic theory we expected that imports from a given country would increase as the price from that country dropped relative to prices from other countries and the domestic price. Imports would increase as domestic demand increased. If there were shifts in imports that could not be explained by the model, we would then use the leading indicators of technological change to determine whether they could explain the residual shit% We used annual data for prices, imports, and spending, and used multiple regression to estimate the coefficients of the model. For domestic market shifts, we used a Fisher-Pry substitution model to explain the replacement of one item by another. This model is given by the following equation: F = f

1-f

= exp[2a(t

- t,,)]

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79

where f

= market share of the new item t0 = time at which substitution is 50% complete (Y = substitution rate (slope of the regression line).

This model actually produces an S-shaped curve for market share of the new item. The effect of dividing market share by fraction of the market not yet captured is to “straighten out” the S-curve so that linear regression can be applied (Ref. 2). We selected commodities for our cases to satisfy four criteria. First, we wanted commodities that had experienced market shift. We initially identified over 30 such commodities by examining import data, trade journals, industry reports, and government reports. Second, we selected commodities that appeared to have been affected by technological change. We applied this criterion during the initial search. Thus the commodities on our initial list had all apparently been affected by technological change. Third, we wanted to examine specific commodities at a low level of aggregation rather than whole industries. We wanted to focus on the level actually used by decision makers in industry and government. For instance, Standard Industry Classification (SIC) code 3572 is “Typewriters.” This was a suitable level of aggregation, since people actually do decide to buy one typewriter rather than another. However, SIC code 3579, “Office Machines, Not Elsewhere Classified, ” included items as disparate as dictating machines and pencil sharpeners. This was unsuitable for our purposes, because no one makes decisions about all the items in this “industry. ” In actuality, we used the level of aggregation of the seven-digit SIC code, since import and export data are available at this level. Fourth, we wanted to examine commodities for which we could obtain data on our technological indicators. This turned out to be the most difficult criterion to satisfy, since much of the data on these indicators was available only at highly aggregated levels. In other cases, the data on indicators was divided into categories in a manner that was simply incompatible with the seven-digit SIC codes. We ultimately did obtain ten commodities that satisfied all our criteria. The imported ones were Nails and Tacks Bicycles Cameras (hand-held still) Electronic calculators Film (still photographic) Motorcycles Television picture tubes Typewriters Watches. The domestic commodities

were

Beverage cans (beer and soft drinks) Milk containers.

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The indicators of technology that we selected were patents, technical papers, and R&D expenditures. Data on R&D expenditures were utilized on an annual basis. However, we felt that taking patents and papers on an annual basis would be inadequate. Patents have a useful life; papers are of some value for several years after publication. We thus took a moving three-year sum of papers as the measure of that indicator. We assumed that the value of a patent decays linearly over a five-year period. Thus our measure of patents was a weighted sum of those taken out during the previous five years, with the most recent patents weighted most heavily. These measures clearly do not fully capture the importance of papers and patents, nor do they take into account differences among technical fields. Nevertheless, we regarded them as improvements over simple annual counts. The Noise Level Since we were treating technology as a residual, we had to determine how much of the behavior of a commodity could be explained by purely economic phenomena. The portion of the behavior that could not be explained by economics, that is, the “noise,” would be a limitation on our ability to determine the effects of technology. Imports of nails and tacks have varied significantly during the period we studied. However, there were no technological changes in these commodities. Import fluctuations were purely the result of supply and demand. Therefore they provided a measure of the ability of our import model to explain changes in level of imports. Any residual here would be simply noise, unexplainable by either economic or technological factors. We found four categories of nails imported in sufficient quantity to be worth examining. Variations in expenditures for construction explained about 66% of the variance in total nail imports, and from 45% to 72% of the variance in imports in the specific categories. But price often entered our regression with the wrong sign. That is, price was higher when imports were higher, contrary to the predictions of our import model. This turned out to be easily explained. Nails are a small portion of the cost of construction. When construction demand rises, nail suppliers can demand and get a higher price. Hence instead of imports rising because price dropped, price rose because imports were rising. Even so, however, we found that price did help explain amount of imports from one country as opposed to another. That is, those exporters who took least advantage of increasing demand were able to sell more nails. The end result of this analysis was that economic factors leave unexplained from 20% to 50% of the variance in imports, even when no technological change has taken place. Strikes and similar factors help explain the remainder, but we could not include these in our analysis. Sample Analyses To illustrate the nature of the analyses, culators, cameras, and milk containers.

we will consider

four cases: bicycles,

cal-

BICYCLES

Bicycles were included because there was a significant increase in imports in the early 197Os, and many of the imports included technology (lightweight frames and tenspeed gears) superior to that available on domestic bicycles. Bicycle sales grew modestly in the United States during the 1960s. Imports increased at the same time. However, the United States was a major exporter of low-priced bicycles.

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Bicycle sales grew dramatically after 197 1, peaking at 15 million in 1974. Imports accounted for a great share of the sales, since domestic manufacturers were already operating at full capacity. Domestic manufacturers expanded capacity as rapidly as possible: one manufacturer tripled its capacity; another increased capacity to what would have been one-half of total 1969 domestic production. The boom ended in 1975. Sales and imports dropped sharply. U.S. exports fell off, as did Japanese exports to the U.S. Consumer demand, not technology, was the factor behind the increase in imports. Domestic manufacturers did not have the capacity to meet demand. Their capacity expansions were not completed until after the boom subsided. While the imports incorporated better technology than did the domestic bicycles, this was not “new” technology. It had been available in Europe for years, and was available on imports for those U.S. citizens who wanted it. Most U.S. purchasers, however, were satisfied with lower-price, lowertechnology domestic bicycles. We could find no indicators of significant technological activity in bicycles in any of the nations exporting to the United States. Hence we concluded that the surge in imports was due solely to economic factors. CALCULATORS

The electronic calculator completely overturned the mechanical calculator market. The firms that used to make mechanical calculators are for the most part no longer in the calculator business. The firms making electronic calculators were never in the mechanical calculator business. The electronic calculator was first produced in Japan and the United Kingdom in the early 1960s. These were based on locally developed technology. However, the United States had a dominant position in computer technology. Thus many of the early imports, especially from Belgium, were assembled from U.S.-made parts. Japanese calculators imported into the United States also had a significant U.S.-made content, the level of which peaked in 197 1. After that, as Japanese technology improved, the U.S.-made content of Japanese calculators declined. Since most calculators imported into the United States were Japanese-made, our analysis focused on indices of Japanese technology. We could not identify patents associated with calculators, since many of the integrated circuits used in calculators have other uses as well. Therefore the Patent Office does not identify them as calculator patents. The figures for R&D expenditures were highly aggregated, and were available only as a total for electronics. There were many technical papers on calculators. Those dealing with applications were screened out, and we considered only papers dealing with calculator technology. There was considerable year-to-year fluctuation in numbers, apparently due more to publication delays than to any other factor. Use of these indicators was not completely satisfactory. Japanese electronic R&D as a percent of U.S. electronic R&D climbed sharply from 5% in 1969 to 25% in 1973. This increase did lead the decline in the U.S.-made content of Japanese calculators by about two years. However, this finding is not conclusive because the Japanese R&D may not have been directed toward calculator technology. The peak of Japanese publications on calculator technology actually followed the increase in Japanese content of imported calculators. This was probably due to deliberate delay in publication. We examined papers on calculators given at U.S. meetings, and found that in general they dealt only with technology already on the market.

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We concluded that the growth in imports of Japanese calculators was clearly due to technology, much of it Japanese-developed. However, only R&D expenditures gave any leading indication of this technology. Papers were published after the technology was on the market, and patents were not usable as an indicator. CAMERAS

Japan is today the “quality camera” capital of the world. Imports of Japanese cameras have grown steadily. However, while the Japanese hold the prestige end of the market, most sales, both units and dollars, are in the “snapshot” end of the market. Eastman Kodak dominated this end of the market in the United States, and continued to export low-priced cameras despite growing imports of quality Japanese cameras. For our analysis we therefore focused on the high-priced cameras. Since most of these came from Japan, we examined only Japanese exports. Economic variables explained up to 30% of the variance. Inclusion of Japanese camera patents raised this to about 62% of the variance, when patent data were lagged two years. Thus we concluded that for high-technology cameras, patents provide a two-year advanced warning of market shifts. The number of papers was too small to use, and we could not obtain data on photographic R&D. Hence these indicators could not be tested. MILK CONTAINERS

In the early 196Os, the milk container market was divided between glass bottles and plastic-coated paper cartons. At that time there was a shift in the distribution of milk. Home delivery declined, and sales at groceries and other stores increased. Consumers demanded larger-sized milk containers from stores. In 1964 wholesale sales of milk accounted for 72% of the total, and 16% of total sales were in gallon containers. By 1977 the wholesale percentage had grown to 95%, and gallon containers to 49%. A major factor in making the gallon container feasible was the blow-molded plastic jug, which was lighter than any competing gallon-sized container, although it was more expensive. We concluded that the shift to blow-molded plastic gallon-sized containers was a response to consumer demand for milk at grocery stores, in a convenient and lightweight container. The question then became whether there was a technolgy index that might have provided advanced warning of this shift. We used the count of papers published in technical and trade journals as an indicator of the availability of new milk container technology. The three-year moving sum rose from virtually zero in 1964 to a peak of about 65 in 1970, then dropped off to about 20 in 1977. The growth in papers provided at least a one-year lead for the growth in use of plastic jugs, which reached 10% of the market in 1969. Note that it is the initial growth in number of papers, not the peak, that provides this advanced warning. By the time the number of papers has peaked, the market shift is already well underway. Conclusions We were surprised at the relatively low explanatory power of our economic models. The aggregate models did quite well. However, when we broke the aggregate market down into individual countries and specific classes of commodities, their explanatory power declined. This indicates that economic models are somewhat limited in their utility. We found that when technological indicators were added to the economic models, their explanatory power was usually improved. Our results for all commodities are sum-

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OF MARKET SHIFT

marized in Table 1. We examined 10 commodities in which technological change had taken place. In 7 of the 10 cases, at least one technology index gave advanced warning. In the one case where no technological change had occurred, neither indicator gave a “false alarm.” Likewise, each indicator we tested gave good results in at least one case. We conclude that these indices of technology can give advanced warning of market shift. However, we cannot explain why some failed when they did, nor can we identify those cases in which success is likely. More research is clearly needed. Nevertheless, the results are encouraging enough that we believe managers should take them into account. Since these indicators seem far more likely to give a false rest than a false alarm, when managers detect an increase in technological activity (patents, papers, R&D expenditures) in an area that may be a threat, they may have only one to three years to respond.

TABLE 1 Years of Lead Time

Commodity Nails and tacks Bicycles Calculators Cameras Film Motorcycles TV picture tubes Typewriters Watches Beverage cans Milk containers

Patents

Papers

P ”

” c ” b r

l-2 b c e 2-3 2 ”
2 b h 1 1

R&D expenditures ” l-2 0 h R r ” e ” R

” Not tested. b Test attempted, but sufficient data not available. c Tested and failed to give useful leading indication.

References 1. Bright, James R., Research. Development, and Technological Innovation, Irwin, Homewood, III., 1964. 2. Fisher, J. C., and Pry, R. H., A Simple Substitution Model of Technological Change, Technological Forecasting and Social Change 3:75-88 (197 1). 3. Martino, Joseph P., Chen, K. L., Weiler, John, and Machnic, John. Tests of Some Indices of Technological Change as Leading Indicators of Market Shift, UDRI-TR-8&35, University of Dayton. Dayton, Ohio, Received 5 November

1981